Demand for high-performance computing is increasing in atmospheric and climate sciences, and in natural sciences in general. Unfortunately, automatic optimizations done by compilers are not enough to make use of target machines' capabilities. Manual code adjustments are mandatory to exploit hardware capabilities. However, optimizing for one architecture, may degrade performance for other architectures. This loss of portability is a challenge. With GGDML we examine an approach for icosahedral-grid based climate and atmospheric models, that is based on a domain-specific language (DSL) which fosters separation of concerns between domain scientists and computer scientists. Our DSL extends Fortran language with concepts from domain science, apart from any technical descriptions such as hardware based optimization. The approach aims to achieve high performance, portability and maintainability through a compilation infrastructure principally built upon configurations from computer scientists. Fortran code extended with novel semantics from the DSL goes through the meta-DSL based compilation procedure. This generates high performance code -aware of platform features, based on provided configurations. We show that our approach reduces code significantly (to 40%) and improves readability for the models DYNAMICO, ICON and NICAM. We also show that the whole approach is viable in terms of performance portability, as it allows to generate platform-optimized code with minimal configuration changes. With a few lines, we are able to switch between two different memory representations during compilation and achieve double the performance. In addition, applying inlining and loop fusion yields 10 percent enhanced performance.

@misc{TPPFAACMWT17,
	author	 = {Nabeeh Jumah and Julian Kunkel and Günther Zängl and Hisashi Yashiro and Thomas Dubos and Yann Meurdesoif},
	title	 = {{Towards Performance Portability for Atmospheric and Climate Models with the GGDML DSL}},
	year	 = {2017},
	month	 = {06},
	location	 = {Germany, Frankfurt},
	activity	 = {ISC 2017},
	abstract	 = {Demand for high-performance computing is increasing in atmospheric and climate sciences, and in natural sciences in general. Unfortunately, automatic optimizations done by compilers are not enough to make use of target machines' capabilities. Manual code adjustments are mandatory to exploit hardware capabilities. However, optimizing for one architecture, may degrade performance for other architectures. This loss of portability is a challenge. With GGDML we examine an approach for icosahedral-grid based climate and atmospheric models, that is based on a domain-specific language (DSL) which fosters separation of concerns between domain scientists and computer scientists. Our DSL extends Fortran language with concepts from domain science, apart from any technical descriptions such as hardware based optimization. The approach aims to achieve high performance, portability and maintainability through a compilation infrastructure principally built upon configurations from computer scientists. Fortran code extended with novel semantics from the DSL goes through the meta-DSL based compilation procedure. This generates high performance code -aware of platform features, based on provided configurations. We show that our approach reduces code significantly (to 40\%) and improves readability for the models DYNAMICO, ICON and NICAM. We also show that the whole approach is viable in terms of performance portability, as it allows to generate platform-optimized code with minimal configuration changes. With a few lines, we are able to switch between two different memory representations during compilation and achieve double the performance. In addition, applying inlining and loop fusion yields 10 percent enhanced performance.},
	url	 = {http://isc-hpc.com/isc17_ap/auftritt/daten/attachments/RP19_Jumah.pdf},
}